The papers we heard today were not picking the low-hanging fruit of text mining. There’s actually a lot of low-hanging fruit out there still worth picking — big questions that are easy to answer quantitatively and that only require organizing large datasets — but these papers were tackling problems that are (for good or ill) inherently more difficult. Part of the reason involves their transnational provenance, but another reason is that they aren’t just counting or mapping known categories but trying to rethink some of the basic concepts we use to write literary history — in particular, the concept we call “influence” or “diffusion” or “intertextuality.”

I’m tossing several terms at this concept because I don’t think literary historians have ever agreed what it should be called. But to put it very naively: new literary patterns originate somehow, and somehow they are reproduced. Different generations of scholars have modeled this differently. Hoyt and Richard quote Laura Riding and Robert Graves exploring, in 1927, an older model centered on basically personal relationships of imitation or influence. But early-twentieth-century scholars could also think anthropologically about the transmission of motifs or myths or A. O. Lovejoy’s “unit ideas.” In the later 20th century, critics got more cautious about implying continuity, and reframed this topic abstractly as “intertextuality.” But then the specificity of New Historicism sometimes pushed us back in the direction of tracing individual sources.

I’m retelling a story you already know, but trying to retell it very frankly, in order to admit that (while we’ve gained some insight) there is also a sense in which literary historians keep returning to the same problem and keep answering it in semi-satisfactory ways. We don’t all, necessarily, aspire to give a causal account of literary change. But I think we keep returning to this problem because we would like to have a kind of narrative that can move more smoothly between individual examples and the level of the discourse or genre. When we’re writing our articles the way this often works in practice is: “here’s one example, two examples — magic hand-waving — a discourse!”

Something interesting and multivocal about literary history gets lost at the moment when we do that hand-waving. The things we call genres or discourses have an internal complexity that may be too big to illustrate with examples, but that also gets lost if you try to condense it into a single label, like “the epistolary novel.” Though we aspire to subtlety, in practice it’s hard to move from individual instances to groups without constructing something like the sovereign in the frontispiece for Hobbes’ Leviathan – a homogenous collection of instances composing a giant body with clear edges.

While they offer different solutions, I think both of the papers we heard today are imagining other ways to move between instances and groups. They both use digital methods to describe new forms of similarity between texts. And in both cases, the point of doing this lies less in precision than in creating a newly flexible model of collectivity. We gain a way of talking about texts that is collective and social, but not necessarily condensed into a single label. For Andrew, the “Werther effect” is less about defining a new genre than about recognizing a new set of relationships between different communities of works. For Hoyt and Richard, machine learning provides a way of talking about the reception of hokku that isn’t limited to formal imitation or to a group of texts obviously “influenced” by specific models. Algorithms help them work outward from clear examples of a literary-historical phenomenon toward a broader penumbra of similarity.

I think this kind of flexibility is one of the most important things digital tools can help us achieve, but I don’t think it’s on many radar screens right now. The reason, I suspect, is that it doesn’t fit our intuitions about computers. We understand that computers can help us with scale (distant reading), and we also get that they can map social networks. But the idea that computers can help us grapple with ambiguity and multiple determination doesn’t feel intuitive. Aren’t computers all about “binary logic”? If I tell my computer that this poem both is and is not a haiku, won’t it probably start to sputter and emit smoke?

Well, maybe not. And actually I think this is a point that should be obvious but just happens to fall in a cultural blind spot right now. The whole point of quantification is to get beyond binary categories — to grapple with questions of degree that aren’t well-represented as yes-or-no questions. Classification algorithms, for instance, are actually very good at shades of gray; they can express predictions as degrees of probability and assign the same text different degrees of membership in as many overlapping categories as you like. So I think it should feel intuitive that a quantitative approach to literary history would have the effect of loosening up categories that we now tend to treat too much as homogenous bodies. If you need to deal with gradients of difference, numbers are your friend.

Of course, how exactly this is going to work remains an open question. Technically, the papers we heard today approach the problem of similarity in different ways. Hoyt and Richard are borrowing machine learning algorithms that use the contrast between groups of texts to define similarity. Andrew’s improvising a different approach that uses a single work to define a set of features that can then be used to organize other works as an “exotext.” And other scholars have approached the same problem in other ways. Franco Moretti’s chapter on “Trees” also bridges the gap I’m talking about between individual examples and coherent discourses; he does it by breaking the genre of detective fiction up into a tree of differentiations. It’s not a computational approach, but for some problems we may not need computation. Matt Jockers, on the other hand, has a chapter on “influence” in Macroanalysis that uses topic modeling to define global criteria of similarity for nineteenth-century novels. And I could go on: Sara Steger, for instance, has done work on sentimentality in the nineteenth century novel that uses machine learning in a loosely analogous way to think about the affective dimension of genre.

The differences between these projects are worth discussing, but in this response I’m more interested in highlighting the common impulse they share. While these projects explore specific problems in literary history, they can also be understood as interventions in literary theory, because they’re all attempting to rethink certain basic concepts we use to organize literary-historical narrative. Andrew’s concept of the “exotext” makes this theoretical ambition most overt, but I think it’s implicit across a range of projects. For me the point of the enterprise, at this stage, is to brainstorm flexible alternatives to our existing, slightly clunky, models of literary collectivity. And what I find exciting at the moment is the sheer proliferation of alternatives.